1. Field of the Invention
The present invention relates to the field of video image processing. More particularly, the present invention is directed to visual odometry methods for a distributed aperture system.
2. Description of the Prior Art
An important aspect of today's computer vision systems is in the recovery and processing of three-dimensional pose (i.e., position and orientation) information associated with mobile video sensors. This is particularly useful in autonomous navigation of vehicles and robots, route visualization, match movement and augmented reality applications. The effective use of video sensors in obstacle detection and navigation has been an ongoing objective in the field of ground vehicle robotics for many years and, as more advanced computational components become available, will continue to be a growing area of interest in the thriving realm of computer vision.
Simultaneous localization and mapping (SLAM) is a technique that is commonly employed in technological areas employing autonomous or preprogrammed tasks, such as in the field of robotics. This particular technique may be used by a robot, for example, to construct a map of an unfamiliar environment while simultaneously keeping track of its current position. However, there are inherent uncertainties in discerning relative movement from various sensors. For example, if there is a slight inaccuracy in the measured distance and direction traveled during an iteration of the mapping sequence, then errors will be traversed to subsequent features added to the map. When these positional errors go unchecked or undetected, the map becomes grossly distorted and, therefore, the ability to precisely determine location becomes significantly compromised.
The SLAM technique is often performed using range type sensors rather than ordinary passive two-dimensional cameras. Typically, the SLAM technique is performed utilizing active three-dimensional laser imagining detection and ranging (LIDAR). Yet, successfully developing a robust SLAM structure from motion systems, which can be configured to function over significantly longer periods of time using video data from passive two-dimensional cameras, continues to remain a challenge.
Considerable amounts of time and research are concentrated in the area of visual odometry. Relatively recent improvements in the performance of both sensors and computing hardware have made real-time vision processing more practical. As computer vision algorithms continue to mature, more visually based navigation systems will become available. Previously published methods for visual odometry have employed video streams from one or two moving cameras in monocular and binocular configurations. In addition, research and development of invariant feature matching has lead to landmark based three-dimensional motion tracking systems.
Although these developments are impressive and continue to contribute to improved methods for implementing visual odometry, they are still deficient in that they lack the robustness necessary for autonomous applications over extended periods of time. Various factors can contribute to and result in the break down of algorithms, such as, the familiar problematic dropping of video frames during turning maneuvers, presence of artifacts during video capture, video frames comprised of minimal image features or no image features at all, significant foreground object motion obscuring an imaged scene and/or considerable sensor motion preventing reliable tracking of image features.
The aforementioned shortcomings are addressed in accordance with the principles of the present invention, wherein an improved visual odometry method for a distributed aperture system is provided.